Mode-locked lasers are now capable of generating pulses whose duration can be as short as a few femtoseconds (one femtosecond=10−15 sec.). There is a broad variety of mode-locked lasers emitting pulses from nanosecond (one nanosecond=10−9 sec.) to femtosecond duration. Short laser pulses can be amplified in laser amplifiers; they can also be temporally compressed externally to laser systems using the combination of spectral broadening in a nonlinear medium, and propagation in a dispersive material. The peak power of the pulses emitted by ultra-fast laser systems can be as low as one watt in mode-locked semiconductor diodes, and can reach the petawatt range (one petawatt=1015 watts) in the highest power systems.
Pulses of laser radiation need to be characterized in both the spectral and temporal domains. The full characterization of pulses would require knowing the pulse intensity and phase in at least one domain (the pulse distribution in the other domain would be given by a Fourier transform). In practice, it turns out that phase information is difficult to obtain, but the intensity information is accessible through the measurements done with various instruments. Phase information, though, is crucial for pulse characterization; it defines the frequency chirp (the drift of laser frequency during the short pulse). Frequency and phase are interrelated; frequency is the derivative of the phase. The phase distribution of a pulse contributes to define the pulse duration; when the phase of a laser pulse is constant throughout the pulse, it is then that the pulse duration is minimum.
In the spectral domain, one uses various types of optical spectrometers (usually grating spectrometers) to obtain directly the power spectral density of the laser pulses,
In the time domain, one does not have detectors fast enough to directly monitor the pulse shape, if the pulse duration is below 10 picoseconds (one picosecond=10−12 sec.). This situation is due to the fact that electronic detectors, in combination with sampling oscilloscopes, can measure optical signals of duration down to 10 picoseconds; this approach does not allow to go much below that limit. There is one exceptionally fast detector, that allows to record the direct pulse shape: it is the streak camera. However this detector is a very expensive instrument (cost>$300,000) that has a limited response time (slightly under one picosecond) and that has a limited sensitivity in the near-infrared, the spectral range used for optical telecommunications.
The limitations of conventional electronic detection systems have forced the users of ultra-fast lasers to investigate indirect methods based on nonlinear optics to obtain information about the pulse shape. In general, for temporal measurements down to the femtosecond range, one uses autocorrelators. These instruments take advantage of an optical non-linearity (second-harmonic generation, two-photon absorption) to produce the correlation of the pulse intensity with itself. Typical autocorrelator setups involve splitting a pulse into two replicas with a variable delay (for instance, with a Michelson interferometer); the two pulses travel in parallel but do not overlap spatially. The pulses are focused in the nonlinear material. The autocorrelation signal is obtained from the second harmonic signal or the photocurrent generated by two-photon absorption.
Autocorrelators are found in every laboratory involved with short pulse lasers. Despite their general use, autocorrelators have a number of drawbacks, the most important being that they do not provide access to the real pulse shape, but to its intensity autocorrelation. The data obtained with autocorrelators can be fitted with various pulse shapes; in practice one cannot discriminate between different pulse shapes. Their use has some practical relevance (easy implementation, commercial availability of autocorrelators) but the information they produce is incomplete. Qualitative information about frequency chirp can be obtained using what is called interferometric autocorrelation; the difference between conventional (intensity) autocorrelation and interferometric autocorrelation is that the two replicas of the pulse under study travel on parallel, distinct axes in the first type of autocorrelators, and travel along the same axis for the second type.
The most often quoted parameter describing short laser pulses is their duration. One generally defines pulse duration as the Full Width at Half Maximum (FWHM) of the pulse intensity distribution in the time domain. This definition of pulse duration leads to some arbitrariness, especially if there are pedestals before or after the pulse, or if the pulse shape is not smooth but exhibits some bumps. For such pulse shapes the duration defined in terms of FWHM is not representative of the entire pulse shape. It should be pointed out that autocorrelation measurements do not provide a direct reading of the pulse FWHM, unless autocorrelation data are fitted with a certain function representative of the pulse shape. But again this procedure leads to arbitrariness in the choice of the fitting function.
In the spectral domain, the pulses are often characterized by their spectral width defined as the FWHM of the power spectral density of the pulses (to shorten the notation we will use “pulse spectrum” as a short hand for “power spectral density of pulses”). Optical spectrometers allow to record directly the pulse spectrum; hence the estimation of the FWHM in the spectral domain does not involve any data fitting with a selected functional form (as for the case of temporal measurements with autocorrelators). Still the relevance of the FWHM can be questioned whenever the pulse spectrum exhibits pedestals or multiple peaks.
It has become a common practice to quote the time-bandwidth product of a pulse. The time-bandwidth product is given by the product of the pulse FWHM's in the time and spectral domains. For a given pulse shape, this parameter assumes a minimum value when the pulse phase is constant (no frequency chirp). The problem with the use of that parameter is that temporal measurements made with autocorrelators are indirect, hence one never knows if the assumed pulse shape is really representative of the true pulse shape.
Advanced diagnostics have recently been developed to remove the arbitrariness behind the interpretation of autocorrelation measurements. The two most quoted methods are labeled FROG (Frequency Resolved Optical Gating) and SPIDER (Spectral Phase Interferometry for Direct Electric field Reconstruction.). These two methods are somewhat more complex than conventional autocorrelators. The interpretation of FROG (D. J. Kane and R. Trebino, IEEE J. Quantum Electron. 29, 571 (1993)) measurements necessitates a complex inversion algorithm; the interpretation of SPIDER (C. laconis and I. A. Walmsley, Opt. Lett. 23, 792 (1998)) measurements does not require such a complex numerical procedure, but the experimental implementation of SPIDER involves splitting the pulse under study into three replicas, one of which would be stretched in time due to an imposed frequency chirp.
Even though these two diagnostics provide a full characterization of laser pulses (at least, in principle), they do not provide any guideline as to how the pulses are going to be modified when they travel through a dispersive material. Any transparent material (various dielectrics such as glass, optical fibers, optical waveguide, etc . . . ) has an index of refraction that changes with optical wavelength (or frequency); this phenomenon is called dispersion. Dispersion has profound effects on the shape of laser pulses propagated in dielectric materials; since the different spectral components (or frequencies) constituting the pulses are traveling at different speeds. Dispersion leads to pulse stretching for input pulses with no chirp. Dispersion can lead to pulse compression if the input pulse possesses a certain chirp, and the dispersive material has a dispersion such that it produces a chirp of opposite sign. To predict how the shape of pulse evolves in a dielectric, one needs to use a numerical algorithm to simulate pulse propagation. Only in exceptional cases, such as Gaussian pulses, one can rely on a purely analytical approach to predict the changes of the pulse parameters; in general, however, this is not possible.